A Secure Grid Medical Data Manager Interfaced to the gLite Middleware

The medical community is producing and manipulating a tremendous volume of digital data for which computerized archiving, processing and analysis is needed. Grid infrastructures are promising for dealing with challenges arising in computerized medicine but the manipulation of medical data on such infrastructures faces both the problem of interconnecting medical information systems to Grid middlewares and of preserving patients’ privacy in a wide and distributed multi-user system. These constraints are often limiting the use of Grids for manipulating sensitive medical data. This paper describes our design of a medical data management system taking advantage of the advanced gLite data management services, developed in the context of the EGEE project, to fulfill the stringent needs of the medical community. It ensures medical data protection through strict data access control, anonymization and encryption. The multi-level access control provides the flexibility needed for implementing complex medical use-cases. Data anonymization prevents the exposure of most sensitive data to unauthorized users, and data encryption guarantees data protection even when it is stored at remote sites. Moreover, the developed prototype provides a Grid storage resource manager (SRM) interface to standard medical DICOM servers thereby enabling transparent access to medical data without interfering with medical practice.     

Contribution of laser microdissection-based technology to proteomic analysis in hepatocellular carcinoma developing on cirrhosis

Hepatocellular carcinoma (HCC) is a major cause of cancer worldwide. Proteomic studies provide opportunities to uncover targets for the diagnosis and treatment of this disease. However, in HCC developing in a setting of cirrhosis, the detection of proteome alterations may be hampered by the increased cellular heterogeneity of tissue when analysing global liver homogenates. The aim of this study was to evaluate whether the identification of proteome alterations in these HCC cases was improved when the differential protein profile between tumour and non-tumour areas of liver was determined using hepatocytes isolated by laser microdissection (LM). Differential profiles established with LM-hepatocytes and liver section homogenates using 2-DE and MS exhibited noticeable differences: 30% of the protein spots with deregulated expression in tumorous LM-samples did not display any modification in homogenates; conversely 15% of proteins altered in tumorous homogenates were not impaired in LM-hepatocytes. These alterations resulted from the presence in cirrhotic liver of fibrotic stroma which displayed a protein pattern different from that determined in LM-hepatocytes. In conclusion, our data demonstrate the interest of LM in distinguishing between fibrotic and hepatocyte proteome alterations and thus the benefit of LM to proteome studies of HCC developing in a context of cirrhosis.     

Digital Image Analysis to Standardize a Photometric Method in Colorimetric Quantification

Abstract

Techniques applying digital images increasingly have been used in biology, medicine, physics, and other research areas. The image coordinates can represent light intensities values to be detected by a CCD. Based on this concept, a photometric system composed of a LED source and a digital camera as a detector was used for optical density measurements. Standards for permanganate, glucose, and protein solutions were determined by colorimetric methods using our device. Samples of protein of Pasteurella mutocida bacteria membrane and, also, fractions of rabbit kidney membrane, rich in Na, K‐ATPase, with unknown concentrations were dosed through the Hartree method using our photometric system.     

Kidney injury and cell therapy: Preclinical study

The aim of this study is to show histological and immunofluorescence analysis of renal parenchyma of agoutis affected by gentamicin‐induced renal disease after the infusion of bone marrow mononuclear cells (BMMC) stained with Hoechst®. Nine agouti's males were divided into three groups: Test group (TG): renal disease by gentamicin induced (n = 3), cell therapy group (CTG): renal disease by gentamicin induced and BMMC infusion (n = 3), and control group (CG): nonrenal disease and BMMC infusion (n = 3). TG and CTG were submitted to the protocol of renal disease induction using weekly application of gentamicin sulfate for 4 months. CG and CTG received a 1 × 108 BMMC stained with Hoechst and were euthanized for kidney examination 21 days after BMMC injection and samples were collected for histology and immunofluorescence analysis. Histological analysis demonstrated typical interstitial lesions in kidney similarly to human disease, as tubular necrosis, glomerular destruction, atrophy tubular, fibrotic areas, and collagen deposition. We conclude that histological analysis suggest a positive application of agouti's as a model for a gentamicin inducing of kidney disease, beyond the immunofluorescence analysis suggest a significant migration of BMMC to sites of renal injury in CTG. Microsc. Res. Tech., 2012. © 2011 Wiley Periodicals, Inc.     

Volatilization of hydrogen sulfide from a quiescent surface

Air-water mass transfer of hydrogen sulfide from a shallow tank with a quiescent surface under the influence of weak wind stress on the water surface was studied numerically using a two-dimensional model. The flow field in the tank was investigated using a computational code based on a finite volume, which is used to numerically solve momentum, mass and continuity conservation equations. The results show that water phase flow field is strongly dependent on the wind-induced surface velocity and the aspect ratio of the tank. Based on the numerical study, the liquid-side mass transfer coefficient is correlated with Reynolds number (R(e)), tank aspect ratio (AR) and Schmidt number (S(c)). Overall mass transfer coefficient (K(L)) values extend further downstream as the R(e) number increases.     

Development of an Autonomous Robot for Gas Storage Spheres Inspection

Inspection for corrosion of gas storage spheres at the welding seam lines must be done periodically. Until now this inspection is being done manually and has a high cost associated to it and a high risk of inspection personel injuries. The Brazilian Petroleum Company, Petrobras, is seeking cost reduction and personel safety by the use of autonomous robot technology. This paper presents the development of a robot capable of autonomously follow a welding line and transporting corrosion measurement sensors. The robot uses a pair of sensors each composed of a laser source and a video camera that allows the estimation of the center of the welding line. The mechanical robot uses four magnetic wheels to adhere to the sphere’s surface and was constructed in a way that always three wheels are in contact with the sphere’s metallic surface which guarantees enough magnetic atraction to hold the robot in the sphere’s surface all the time. Additionally, an independently actuated table for attaching the corrosion inspection sensors was included for small position corrections. Tests were conducted at the laboratory and in a real sphere showing the validity of the proposed approach and implementation.     

A constructive algorithm to synthesize arbitrarily connected feedforward neural networks

In this work we present a constructive algorithm capable of producing arbitrarily connected feedforward neural network architectures for classification problems. Architecture and synaptic weights of the neural network should be defined by the learning procedure. The main purpose is to obtain a parsimonious neural network, in the form of a hybrid and dedicate linear/nonlinear classification model, which can guide to high levels of performance in terms of generalization. Though not being a global optimization algorithm, nor a population-based metaheuristics, the constructive approach has mechanisms to avoid premature convergence, by mixing growing and pruning processes, and also by implementing a relaxation strategy for the learning error. The synaptic weights of the neural networks produced by the constructive mechanism are adjusted by a quasi-Newton method, and the decision to grow or prune the current network is based on a mutual information criterion. A set of benchmark experiments, including artificial and real datasets, indicates that the new proposal presents a favorable performance when compared with alternative approaches in the literature, such as traditional MLP, mixture of heterogeneous experts, cascade correlation networks and an evolutionary programming system, in terms of both classification accuracy and parsimony of the obtained classifier.     

BENCHMARKING RIVER HABITAT IMPROVEMENT

River ecosystems have witnessed a long history of human pressure, particularly the disruption of freshwater fish populations. The awareness of this situation has led to many habitat improvement projects, with a variable degree of success. In natural situations, fish populations co‐inhabit throughout the hydrological cycle with different degrees of adequacy, and the sequence of favourable and unfavourable conditions dictates abiotic constraints and biotic interactions that shape the final biological assemblages. We postulate that a part of unsuccessful restoration results is related to insufficient closeness to the natural habitat conditions of the river type that is to be restored, including the naturally adverse periods. We used the river2d model to predict habitat availability as weighted usable area (WUA) at a degraded site that is to be restored, for two native Mediterranean species and their life stages—the Southwestern nase Iberochondrostoma almacai and the Arade chub Squalius aradensis. We then analysed the yearly evolution of the natural WUA at a nearby reference site. Overall, the reference site exhibited the longest periods during which the WUA was continuously lower than the chosen WUA thresholds for each of the four bioperiods. Considerable divergences from natural habitat availability values can be seen for the spawning, rearing and growth bioperiods. Restoration outcomes can result in appreciable deviations—favourable or unfavourable to fish populations—from the WUA occurring under natural conditions over the course of the year. Restoration should therefore take account of local hydraulic and habitat patterns that govern population dynamics and result in the final fish assemblage. Copyright © 2011 John Wiley & Sons, Ltd.     

Least squares support vector machines with tuning based on chaotic differential evolution approach applied to the identification of a thermal process

In the past decade, support vector machines (SVMs) have gained the attention of many researchers. SVMs are non-parametric supervised learning schemes that rely on statistical learning theory which enables learning machines to generalize well to unseen data. SVMs refer to kernel-based methods that have been introduced as a robust approach to classification and regression problems, lately has handled nonlinear identification problems, the so called support vector regression. In SVMs designs for nonlinear identification, a nonlinear model is represented by an expansion in terms of nonlinear mappings of the model input. The nonlinear mappings define a feature space, which may have infinite dimension. In this context, a relevant identification approach is the least squares support vector machines (LS-SVMs). Compared to the other identification method, LS-SVMs possess prominent advantages: its generalization performance (i.e. error rates on test sets) either matches or is significantly better than that of the competing methods, and more importantly, the performance does not depend on the dimensionality of the input data. Consider a constrained optimization problem of quadratic programing with a regularized cost function, the training process of LS-SVM involves the selection of kernel parameters and the regularization parameter of the objective function. A good choice of these parameters is crucial for the performance of the estimator. In this paper, the LS-SVMs design proposed is the combination of LS-SVM and a new chaotic differential evolution optimization approach based on Ikeda map (CDEK). The CDEK is adopted in tuning of regularization parameter and the radial basis function bandwith. Simulations using LS-SVMs on NARX (Nonlinear AutoRegressive with eXogenous inputs) for the identification of a thermal process show the effectiveness and practicality of the proposed CDEK algorithm when compared with the classical DE approach.